Papers
6
Total Citations
187
H-Index
6
About
Shunlin Liang is a leading figure in quantitative remote sensing, with a career dedicated to advancing the retrieval of critical land surface and atmospheric parameters from satellite data. His primary research areas include aerosol optical depth (AOD) estimation, land surface reflectance, and albedo product development. Liang's major contributions are pioneering algorithms that overcome the long-standing challenge of retrieving AOD over complex land surfaces, particularly bright areas. His seminal 2006 paper on improved AOD estimation from MODIS imagery (81 citations) laid the groundwork for a series of innovative methods, including a coupled BRDF and aerosol retrieval model and a high-resolution 30m AOD product using Landsat and machine learning (2022, 20 citations). He has also been instrumental in developing operational directional reflectance and albedo products for next-generation geostationary satellites like GOES-R and Himawari (2019, 48 citations), enabling unprecedented monitoring of rapid surface changes. With over 180 total citations across his most-cited works, Liang's research has profoundly impacted climate modeling, urban air pollution monitoring, and environmental assessment, establishing him as a key architect of modern Earth observation science.
Research Focus
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